Enhancing recommender systems by incorporating social information

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• 作者：Li-wei Huang ; Gui-sheng Chen ; Yu-chao Liu&#8230
• 关键词：Recommender system ; Social information ; Factor graph model ; TP301.6
• 刊名：Frontiers of Information Technology & Electronic Engineering
• 出版年：2013
• 出版时间：September 2013
• 年：2013
• 卷：14
• 期：9
• 页码：711-721
• 全文大小：588 KB
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• 作者单位：Li-wei Huang (1) (2)
  Gui-sheng Chen (2)
  Yu-chao Liu (2)
  De-yi Li (2)
  
  1. Institute of Command Information System, PLA University of Science and Technology, Nanjing, 210007, China
  2. Institute of Electronic System Engineering, Beijing, 100039, China
  
• 刊物类别：Computer Science, general; Electrical Engineering; Computer Hardware; Computer Systems Organization
• 刊物主题：Computer Science, general; Electrical Engineering; Computer Hardware; Computer Systems Organization and Communication Networks; Electronics and Microelectronics, Instrumentation; Communications Engine
• 出版者：Zhejiang University Press
• ISSN：2095-9230

文摘

Although recommendation techniques have achieved distinct developments over the decades, the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality. Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings. How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge. In this paper, based on a factor graph model, we formalize the problem in a semi-supervised probabilistic model, which can incorporate different user information, user relationships, and user-item ratings for learning to predict the unknown ratings. We evaluate the method in two different genres of datasets, Douban and Last.fm. Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms. Furthermore, a distributed learning algorithm is developed to scale up the approach to real large datasets. Key words Recommender system Social information Factor graph model